arXiv subjects

Paul Xing

Publications and source records attributed to Paul Xing.

10 recordsLinked to original sources

arXiv2026

ULMShare: A Large-Scale In Vivo Ultrasound Localization Microscopy Dataset for Microvascular Imaging

Ultrasound Localization Microscopy (ULM) enables microscopic imaging of the cerebral microvasculature in vivo, but relies on a multi-stage processing pipeline in which acquisition settings and reconstruction processes strongly influence the final output. Existing public datasets remain sparse, restricting rigorous evaluation and slowing progress in algorithm development, including emerging machine-learning approaches, which by design require large quantities of data to be robust and reliable. We introduce \textbf{ULMShare}, an open-access dataset of 99 whole-brain transcranial ULM acquisitions from 61 healthy mice (36 females, 22 males, 3 unknown; mean age: $8.2 \pm 5.5$ weeks; mean weight: $17.7 \pm 4.2$ g), for a total of 30TB of raw data. The dataset spans three experimental procedures, multiple injection and anesthesia protocols, two ultrasound probes, and different imaging planes and orientations. Each acquisition includes raw ultrasonic data, detailed metadata, an illustrative reconstruction and the corresponding microbubble trajectories. Alongside the data, we report vascular saturation, Fourier Ring Correlation, and track-length statistics, plus expert visual gradings. ULMShare provides a broad, standardized and publicly available resource for method development, validation, and benchmarking. The full dataset is available on the Federated Research Data Repository and additional resources are hosted on the ULMShare Github repository.

physics.med-ph↗

arXiv2026

Automatic Aberration Correction for Transcranial Functional and Super-Resolution Ultrasound Imaging in Rodents and Nonhuman Primates

Skull-induced aberrations remain a major drawback of transcranial ultrasound localization microscopy (ULM), degrading sensitivity and spatial accuracy through microbubble mislocalization, false detections, and imaging artifacts, such as disconnected or duplicated vessels. Here, we present a differentiable beamforming framework for automatic aberration correction in transcranial Doppler and ULM. Our approach uses spatially distributed delay-based parameterization of the aberration that is optimized in a closed-loop manner using angular coherence as an objective function. We demonstrate robust improvements of transcranial ULM, in vivo, with enhanced resolution of both mouse and nonhuman primate (NHP) brains. We also extended differentiable beamforming to functional measurements, with improvements in the sensitivity of transcranial functional ultrasound (fUS) and ULM based hemodynamic quantification. Extending this approach to 3D transcranial ULM imaging in NHPs, we show efficient correction of skull induced aberrations and removal of artifacts, such as vessel duplications. By providing a fully automated and generalizable solution for aberration correction, this work lowers a major technical barrier to transcranial ultrasound imaging, enabling broader adoption of non-invasive, super-resolution and functional neuroimaging across laboratories and across species.

physics.med-ph↗

arXiv2024

3D transcranial Dynamic Ultrasound Localization Microscopy in the mouse brain using a Row-Column Array

The role of brain hemodynamics in neurodegenerative diseases cannot be fully assessed using existing imaging technologies. Recently, 2D Dynamic Ultrasound Localization Microscopy (DULM) has allowed for the quantitative mapping of the pulsatile flow at sub-wavelength resolution. However, to obtain accurate velocity estimates, 3D imaging is more adapted, especially for complex vascularized organs like the brain. 3D+t DULM is achievable using matrix array probes, but suffers from limitations in terms of cost, device complexity associated with the high channel count, and operating frequencies. Alternatively, Row Column Arrays (RCA) can reduce the number of elements while maintaining a large field of view and high frame rate. Herein, we demonstrate the feasibility of performing 3D+t blood flow measurements in the mouse brain using an RCA and a DULM sequence with a high spatiotemporal resolution. Transcranial images of anesthetized mice (n=7) were acquired at a volume rate of 750 Hz using 42 tilted plane waves. After microbubbles localization and tracking, super-resolved dynamic density and velocity maps of the 3D brain vascular network were obtained. Cortical vessels were segmented and pulsatility in the arteries was significantly higher than in veins for all mice, in accordance with the literature. Our results demonstrate the feasibility and reproducibility of achieving high spatiotemporal resolution volumes of the mouse brain vasculature with DULM using a RCA.

physics.med-ph↗

arXiv2024

Towards Transcranial 3D Ultrasound Localization Microscopy of the Nonhuman Primate Brain

Hemodynamic changes occur in stroke and neurodegenerative diseases. Developing imaging techniques allowing the in vivo visualization and quantification of cerebral blood flow would help better understand the underlying mechanism of those cerebrovascular diseases. 3D ultrasound localization microscopy (ULM) is a novel technology that can map the microvasculature of the brain at large depth and has been mainly used until now in rodents. Here, we demonstrated the feasibility of 3D ULM of the nonhuman primate (NHP) brain with a single 256-channels programmable ultrasound scanner. We achieved a highly resolved vascular map of the macaque brain at large depth in presence of craniotomy and durectomy using an 8-MHz multiplexed matrix probe. We were able to distinguish vessels as small as 26.9 {\mu}m. We also demonstrated that transcranial imaging of the macaque brain at similar depth was feasible using a 3-MHz probe and achieved a resolution of 60.4 {\mu}m. This work paves the way to clinical application of 3D ULM.

eess.IV↗

arXiv2024

Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy

Ultrasound Localization Microscopy (ULM) is a non-invasive technique that allows for the imaging of micro-vessels in vivo, at depth and with a resolution on the order of ten microns. ULM is based on the sub-resolution localization of individual microbubbles injected in the bloodstream. Mapping the whole angioarchitecture requires the accumulation of microbubbles trajectories from thousands of frames, typically acquired over a few minutes. ULM acquisition times can be reduced by increasing the microbubble concentration, but requires more advanced algorithms to detect them individually. Several deep learning approaches have been proposed for this task, but they remain limited to 2D imaging, in part due to the associated large memory requirements. Herein, we propose to use sparse tensor neural networks to reduce memory usage in 2D and to improve the scaling of the memory requirement for the extension of deep learning architecture to 3D. We study several approaches to efficiently convert ultrasound data into a sparse format and study the impact of the associated loss of information. When applied in 2D, the sparse formulation reduces the memory requirements by a factor 2 at the cost of a small reduction of performance when compared against dense networks. In 3D, the proposed approach reduces memory requirements by two order of magnitude while largely outperforming conventional ULM in high concentration settings. We show that Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense deep learning based method in 2D ULM i.e. the use of higher concentration in silico and reduced acquisition time.

eess.IV↗

arXiv2024

Inverse Problem Approach to Aberration Correction for in vivo Transcranial Imaging Based on a Sparse Representation of Contrast-enhanced Ultrasound Data

Transcranial ultrasound imaging is currently limited by attenuation and aberration induced by the skull. First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM). Herein, we develop an inverse problem approach to aberration correction (IPAC) that leverages the sparsity of microbubble signals. We propose to use the \textit{a priori} knowledge of the medium based upon microbubble localization and wave propagation to build a forward model to link the measured signals directly to the aberration function. A standard least-squares inversion is then used to retrieve the aberration function. We first validated IPAC on simulated data of a vascular network using plane wave as well as divergent wave emissions. We then evaluated the reproducibility of IPAC \textit{in vivo} in 5 mouse brains. We showed that aberration correction improved the contrast of CEUS images by 4.6 dB. For ULM images, IPAC yielded sharper vessels, reduced vessel duplications, and improved the resolution from 21.1 $\mu$m to 18.3 $\mu$m. Aberration correction also improved hemodynamic quantification for velocity magnitude and flow direction.

eess.IV↗

arXiv2023

A unified framework combining coherent compounding, harmonic imaging and angular coherence for simultaneous high-quality B-mode and tissue Doppler in ultrafast echocardiography

Various methods have been proposed to enhance image quality in ultrafast ultrasound. Coherent compounding can improve image quality using multiple steered diverging transmits when motion occurring between transmits is corrected for. Harmonic imaging, a standard technique in conventional focused echocardiography, has been adapted for ultrafast imaging, reducing clutter. Coherence-based approaches have also been shown to increase contrast in clinical settings by enhancing signals from coherent echoes and reducing clutter. Herein, we introduce a simple, unified framework that combines motion-correction, harmonic imaging, and angular-coherence, showing for the first time that their benefits can be combined in real-time. Validation was conducted through in vitro testing on a spinning disk model and in vivo on 4 volunteers. In vitro results confirmed the unified framework capability to achieve high contrast in large-motion contexts up to 17 cm/s. In vivo testing highlighted proficiency in generating images of high quality during low and high tissue velocity phases of the cardiac cycle. Specifically, during ventricular filling, the unified framework increased the gCNR from 0.47 to 0.87 when compared against coherent compounding.

physics.bio-ph↗

arXiv2023

Dynamic Imaging using any Ultrasound Localization Microscopy Dataset

Ultrasound Localization Microscopy (ULM) relies on the injection of microbubbles (MBs) to obtain highly resolved density maps of blood circulation in vivo, with a resolution that can reach 10 {\mu}m ~ {\lambda}/10 in the rodent brain. Static mean velocity maps can be extracted but are intrinsically biased by potential significant changes in the number of MBs detected during the cardiac cycle. Dynamic ULM (DULM) is a technique developed for non-invasive pulsatility measurements in the brain of rodents, leading to temporally resolved velocity and density cine-loops. It was previously based on external triggers such as the electrocardiogram (ECG), limiting its use to datasets acquired specifically for DULM applications while also increasing the required acquisition time. This study presents a new motion matching method using tissue Doppler that eliminates the need for ECG-gating in DULM experiments. DULM can now be performed on any ULM datasets, recovering pertinent temporal information, and improving the robustness of the mean velocity estimates.

physics.med-ph↗

arXiv2023

A Tracking prior to Localization workflow for Ultrasound Localization Microscopy

Ultrasound Localization Microscopy (ULM) has proven effective in resolving microvascular structures and local mean velocities at sub-diffraction-limited scales, offering high-resolution imaging capabilities. Dynamic ULM (DULM) enables the creation of angiography or velocity movies throughout cardiac cycles. Currently, these techniques rely on a Localization-and-Tracking (LAT) workflow consisting in detecting microbubbles (MB) in the frames before pairing them to generate tracks. While conventional LAT methods perform well at low concentrations, they suffer from longer acquisition times and degraded localization and tracking accuracy at higher concentrations, leading to biased angiogram reconstruction and velocity estimation. In this study, we propose a novel approach to address these challenges by reversing the current workflow. The proposed method, Tracking-and-Localization (TAL), relies on first tracking the MB and then performing localization. Through comprehensive benchmarking using both in silico and in vivo experiments and employing various metrics to quantify ULM angiography and velocity maps, we demonstrate that the TAL method consistently outperforms the reference LAT workflow. Moreover, when applied to DULM, TAL successfully extracts velocity variations along the cardiac cycle with improved repeatability. The findings of this work highlight the effectiveness of the TAL approach in overcoming the limitations of conventional LAT methods, providing enhanced ULM angiography and velocity imaging.

physics.med-ph↗

arXiv2022

Phase Aberration Correction for in vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network

Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers (\mu m). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein, we propose a deep learning approach based on complex-valued convolutional neural networks (CV-CNNs) to retrieve the aberration function, which can then be used to form enhanced images using standard delay-and-sum beamforming. CV-CNNs were selected as they can apply time delays through multiplication with in-phase quadrature input data. Predicting the aberration function rather than corrected images also confers enhanced explainability to the network. In addition, 3D spatiotemporal convolutions were used for the network to leverage entire microbubble tracks. For training and validation, we used an anatomically and hemodynamically realistic mouse brain microvascular network model to simulate the flow of microbubbles in presence of aberration. The proposed CV-CNN performance was compared to the coherence-based method by using microbubble tracks. We then confirmed the capability of the proposed network to generalize to transcranial \textit{in vivo} data in the mouse brain (n=3). Vascular reconstructions using a locally predicted aberration function included additional and sharper vessels. The CV-CNN was more robust than the coherence-based method and could perform aberration correction in a 6-month-old mouse. After correction, we measured a resolution of 15.6 \mu m for younger mice, representing an improvement of 25.8 $\%$, while the resolution was improved by 13.9 $\%$ for the 6-month-old mouse. This work leads to different applications for complex-valued convolutions in biomedical imaging and strategies to perform transcranial ULM.

eess.IV↗